{"id":"W4315750697","doi":"10.1139/cgj-2022-0365","title":"Predicting geological interfaces using stacking ensemble learning with multi-scale features","year":2023,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation Singapore; Nemzeti Fejlesztési Minisztérium; National Research Foundation; Strong","keywords":"Interpolation (computer graphics); Computer science; Scale (ratio); Data mining; Process (computing); Borehole; Machine learning; Ensemble learning; Base (topology); Variation (astronomy); Sampling (signal processing); Artificial intelligence; Engineering; Mathematics; Image (mathematics); Geotechnical engineering; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009736221,0.001128774,0.0009847516,0.001610833,0.0003474632,0.0008174867,0.001214208,0.0008067687,0.0006709021],"category_scores_gemma":[0.001700485,0.000342402,0.001168521,0.001170853,0.0003275143,0.001474971,0.001174909,0.001060951,0.000295176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666329,"about_ca_system_score_gemma":0.0005082897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005318372,"about_ca_topic_score_gemma":0.008442333,"domain_scores_codex":[0.9996854,0.00005916435,0.00001799905,0.0001057728,0.0000723479,0.00005929246],"domain_scores_gemma":[0.9992041,0.0002939093,0.0001038265,0.0001237274,0.000211807,0.0000625843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006460617,0.0001311764,0.01694641,0.00002708938,0.0001434513,0.0001003741,0.00005742566,0.8594559,0.003148828,0.0007246152,0.001115837,0.1180843],"study_design_scores_gemma":[0.000001238022,0.00001422549,0.0008509058,0.000002170547,0.000008589157,0.000005897479,0.000009405489,0.9982483,0.000374462,0.0003916114,0.00008970795,0.000003413189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2035018,0.0004603017,0.792681,0.0001427469,0.00005980422,0.0000360532,0.0003428111,0.001510721,0.001264807],"genre_scores_gemma":[0.9159615,0.0001656884,0.08196836,0.0000667435,0.00004579529,0.00003996741,0.0008974808,0.00005939608,0.0007951069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005318372,"threshold_uncertainty_score":0.01057482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557309895685054,"score_gpt":0.2356425558352176,"score_spread":0.2100694568783671,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}